meta-iNat [1]}, [2]} is a benchmark of animal species in the wild. This benchmark is particularly difficult, as classes are unbalanced, distinctions are fine-grained, and images are not cropped or centered, and may contain multiple animal instances. We follow the class split proposed by [1]}: of 1135 classes with between 50 and 1000 images, one fifth (227) are assigned to evaluation and the rest to training. While [1]} propose a full 227-way, \(k\) -shot evaluation scheme with \(10\le k\le 200\) , we instead perform standard 5-way, 1-shot and 5-shot evaluation, and leave extension to higher shots and unbalanced classes for future work.
